[jira] [Created] (SPARK-23246) (Py)Spark OOM because of metadata build-up that cannot be cleaned

["MBA Learns to Code (JIRA)" <ji...@apache.org>]

MBA Learns to Code created SPARK-23246:
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             Summary: (Py)Spark OOM because of metadata build-up that cannot be cleaned
                 Key: SPARK-23246
                 URL: https://issues.apache.org/jira/browse/SPARK-23246
             Project: Spark
          Issue Type: Bug
          Components: PySpark, Spark Core, SQL
    Affects Versions: 2.2.1
            Reporter: MBA Learns to Code


I am having consistent OOM crashes when trying to use PySpark for iterative algorithms in which I create new DataFrames per iteration (e.g. by sampling from a "mother" DataFrame), so something with such DataFrames, and never need such DataFrames ever in future iterations.

The below script simulates such OOM failures. Even when one tries explicitly .unpersist() the temporary DataFrames (by using the --unpersist flag below) and/or deleting and garbage-collecting the Python objects (by using the --py-gc flag below), the Java objects seem to stay on and accumulate until they exceed the JVM/driver memory.

Please suggest how I may overcome this so that we can have long-running iterative programs using Spark that uses resources only up to a bounded, controllable limit.

 
{code:java}
from __future__ import print_function

import argparse
import gc
import pandas

import pyspark


arg_parser = argparse.ArgumentParser()
arg_parser.add_argument('--unpersist', action='store_true')
arg_parser.add_argument('--py-gc', action='store_true')
arg_parser.add_argument('--n-partitions', type=int, default=1000)
args = arg_parser.parse_args()


# create SparkSession (*** set spark.driver.memory to 512m in spark-defaults.conf ***)
spark = pyspark.sql.SparkSession.builder \
    .config('spark.executor.instances', '2') \
    .config('spark.executor.cores', '2') \
    .config('spark.executor.memory', '512m') \
    .enableHiveSupport() \
    .getOrCreate()


# create Parquet file to subsequent repeated loading
df = spark.createDataFrame(
    pandas.DataFrame(
        dict(
            row=range(args.n_partitions),
            x=args.n_partitions * [0]
        )
    )
)

parquet_path = '/tmp/TestOOM-{}Partitions.parquet'.format(args.n_partitions)

df.write.parquet(
    path=parquet_path,
    partitionBy='row',
    mode='overwrite'
)


i = 0


# the below loop simulates an iterative algorithm that creates new DataFrames in each iteration (e.g. sampling from a "mother" DataFrame), do something, and never need those DataFrames again in future iteration
# we are having a problem cleaning up the built-up metadata
# hence the program will crash after while because of OOM
while True:
    _df = spark.read.parquet(parquet_path)

    if args.unpersist:
        _df.unpersist()

    if args.py_gc:
        del _df
        gc.collect()

    i += 1; print('COMPLETED READ ITERATION #{}\n'.format(i))
{code}
 



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